{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sat</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  friend\n",
       "0  2016      1    1   Fri      45      45     45.6      45      29\n",
       "1  2016      1    2   Sat      44      45     45.7      44      61\n",
       "2  2016      1    3   Sun      45      44     45.8      41      56\n",
       "3  2016      1    4   Mon      44      41     45.9      40      53\n",
       "4  2016      1    5  Tues      41      40     46.0      44      41"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.read_csv('temps.csv')\n",
    "\n",
    "#看看数据长什么样子\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据表中\n",
    "* year,moth,day,week分别表示的具体的时间\n",
    "* temp_2：前天的最高温度值\n",
    "* temp_1：昨天的最高温度值\n",
    "* average：在历史中，每年这一天的平均最高温度值\n",
    "* actual：这就是我们的标签值了，当天的真实最高温度\n",
    "* friend：这一列可能是凑热闹的，你的朋友猜测的可能值，咱们不管它就好了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据维度: (348, 9)\n"
     ]
    }
   ],
   "source": [
    "print('数据维度:', features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 处理时间数据\n",
    "import datetime\n",
    "\n",
    "# 分别得到年，月，日\n",
    "years = features['year']\n",
    "months = features['month']\n",
    "days = features['day']\n",
    "\n",
    "# datetime格式\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2016, 1, 1, 0, 0),\n",
       " datetime.datetime(2016, 1, 2, 0, 0),\n",
       " datetime.datetime(2016, 1, 3, 0, 0),\n",
       " datetime.datetime(2016, 1, 4, 0, 0),\n",
       " datetime.datetime(2016, 1, 5, 0, 0)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XL+P9998XPefy5cvo0aMHdDodunTpgh49eiA2NhYajQZXrlzBN998I/iMuP/++1GrVi0U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FCxdi4cKF0Ol0mDt3LkaNGoXdu3eLUmPPGn+d+ji70k0SjsMPw2LxyfFy/HSxGh2i5UjXyvBQ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\ngg==\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 准备画图\n",
    "# 指定默认风格\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# 设置布局\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))\n",
    "fig.autofmt_xdate(rotation = 45)\n",
    "\n",
    "# 标签值\n",
    "ax1.plot(dates, features['actual'])\n",
    "ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')\n",
    "\n",
    "# 昨天\n",
    "ax2.plot(dates, features['temp_1'])\n",
    "ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')\n",
    "\n",
    "# 前天\n",
    "ax3.plot(dates, features['temp_2'])\n",
    "ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')\n",
    "\n",
    "# 我的逗逼朋友\n",
    "ax4.plot(dates, features['friend'])\n",
    "ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')\n",
    "\n",
    "plt.tight_layout(pad=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "      <th>week_Fri</th>\n",
       "      <th>week_Mon</th>\n",
       "      <th>week_Sat</th>\n",
       "      <th>week_Sun</th>\n",
       "      <th>week_Thurs</th>\n",
       "      <th>week_Tues</th>\n",
       "      <th>week_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n",
       "0  2016      1    1      45      45     45.6      45      29         1   \n",
       "1  2016      1    2      44      45     45.7      44      61         0   \n",
       "2  2016      1    3      45      44     45.8      41      56         0   \n",
       "3  2016      1    4      44      41     45.9      40      53         0   \n",
       "4  2016      1    5      41      40     46.0      44      41         0   \n",
       "\n",
       "   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         0         0         0           0          0         0  \n",
       "1         0         1         0           0          0         0  \n",
       "2         0         0         1           0          0         0  \n",
       "3         1         0         0           0          0         0  \n",
       "4         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码\n",
    "features = pd.get_dummies(features)\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 标签\n",
    "labels = np.array(features['actual'])\n",
    "\n",
    "# 在特征中去掉标签\n",
    "features= features.drop('actual', axis = 1)\n",
    "\n",
    "# 名字单独保存一下，以备后患\n",
    "feature_list = list(features.columns)\n",
    "\n",
    "# 转换成合适的格式\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "input_features = preprocessing.StandardScaler().fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -1.5678393 , -1.65682171, -1.48452388, -1.49443549,\n",
       "       -1.3470703 , -1.98891668,  2.44131112, -0.40482045, -0.40961596,\n",
       "       -0.40482045, -0.40482045, -0.41913682, -0.40482045])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_features[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: tensor(8347.9924, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(152.3170, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(145.9625, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(143.9453, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(142.8161, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(142.0664, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(141.5386, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(141.1528, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(140.8618, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(140.6318, dtype=torch.float64, grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor(input_features, dtype = float)\n",
    "\n",
    "y = torch.tensor(labels, dtype = float)\n",
    "\n",
    "# 权重参数初始化\n",
    "weights = torch.randn((14, 128), dtype = float, requires_grad = True) \n",
    "biases = torch.randn(128, dtype = float, requires_grad = True) \n",
    "weights2 = torch.randn((128, 1), dtype = float, requires_grad = True) \n",
    "biases2 = torch.randn(1, dtype = float, requires_grad = True) \n",
    "\n",
    "learning_rate = 0.001 \n",
    "losses = []\n",
    "\n",
    "for i in range(1000):\n",
    "    # 计算隐层\n",
    "    hidden = x.mm(weights) + biases\n",
    "    # 加入激活函数\n",
    "    hidden = torch.relu(hidden)\n",
    "    # 预测结果\n",
    "    predictions = hidden.mm(weights2) + biases2\n",
    "    # 通计算损失\n",
    "    loss = torch.mean((predictions - y) ** 2) \n",
    "    losses.append(loss.data.numpy())\n",
    "    \n",
    "    # 打印损失值\n",
    "    if i % 100 == 0:\n",
    "        print('loss:', loss)\n",
    "    #返向传播计算\n",
    "    loss.backward()\n",
    "    \n",
    "    #更新参数\n",
    "    weights.data.add_(- learning_rate * weights.grad.data)  \n",
    "    biases.data.add_(- learning_rate * biases.grad.data)\n",
    "    weights2.data.add_(- learning_rate * weights2.grad.data)\n",
    "    biases2.data.add_(- learning_rate * biases2.grad.data)\n",
    "    \n",
    "    # 每次迭代都得记得清空\n",
    "    weights.grad.data.zero_()\n",
    "    biases.grad.data.zero_()\n",
    "    weights2.grad.data.zero_()\n",
    "    biases2.grad.data.zero_()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([348, 1])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更简单的构建网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_size = input_features.shape[1]\n",
    "hidden_size = 128\n",
    "output_size = 1\n",
    "batch_size = 16\n",
    "my_nn = torch.nn.Sequential(\n",
    "    torch.nn.Linear(input_size, hidden_size),\n",
    "    torch.nn.Sigmoid(),\n",
    "    torch.nn.Linear(hidden_size, output_size),\n",
    ")\n",
    "cost = torch.nn.MSELoss(reduction='mean')\n",
    "optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3950.7627\n",
      "100 37.9201\n",
      "200 35.654438\n",
      "300 35.278366\n",
      "400 35.116814\n",
      "500 34.986076\n",
      "600 34.868954\n",
      "700 34.75414\n",
      "800 34.637356\n",
      "900 34.516705\n"
     ]
    }
   ],
   "source": [
    "# 训练网络\n",
    "losses = []\n",
    "for i in range(1000):\n",
    "    batch_loss = []\n",
    "    # MINI-Batch方法来进行训练\n",
    "    for start in range(0, len(input_features), batch_size):\n",
    "        end = start + batch_size if start + batch_size < len(input_features) else len(input_features)\n",
    "        xx = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True)\n",
    "        yy = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True)\n",
    "        prediction = my_nn(xx)\n",
    "        loss = cost(prediction, yy)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward(retain_graph=True)\n",
    "        optimizer.step()\n",
    "        batch_loss.append(loss.data.numpy())\n",
    "    \n",
    "    # 打印损失\n",
    "    if i % 100==0:\n",
    "        losses.append(np.mean(batch_loss))\n",
    "        print(i, np.mean(batch_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = torch.tensor(input_features, dtype = torch.float)\n",
    "predict = my_nn(x).data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 转换日期格式\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n",
    "\n",
    "# 创建一个表格来存日期和其对应的标签数值\n",
    "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})\n",
    "\n",
    "# 同理，再创建一个来存日期和其对应的模型预测值\n",
    "months = features[:, feature_list.index('month')]\n",
    "days = features[:, feature_list.index('day')]\n",
    "years = features[:, feature_list.index('year')]\n",
    "\n",
    "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "\n",
    "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]\n",
    "\n",
    "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predict.reshape(-1)}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//jx//vknI0eO1HDvVahQAaVSydKlS4mOjmbLli0sX778xT+AAkCvqN29e5fa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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 真实值\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')\n",
    "\n",
    "# 预测值\n",
    "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\n",
    "plt.xticks(rotation = '60'); \n",
    "plt.legend()\n",
    "\n",
    "# 图名\n",
    "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
